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Proceedings Paper

Enhanced face alignment using an unsupervised roll estimation initialization
Author(s): Cheng Li; Arash Pourtaherian; W. E. Tjon a Ten; Peter H. N. de With
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Paper Abstract

We propose a novel and efficient initialization method for generalized facial landmark localization with an unsupervised roll-angle estimation based on B-spline models. We first show that the roll angle is crucial for an accurate landmark localization. Therefore, we develop an unsupervised roll-angle estimation by adopting a joint 1st -order B-spline model, which is robust to intensity variations and generic for application to various face detectors. The method consists of three steps. First, the scaled-normalized Laplacian of Gaussian operator is applied to a bounding box generated by a face detector for extracting facial feature segments. Second, a joint 1 st -order B-spline model is fitted to the extracted facial feature segments, using an iterative optimization method. Finally, the roll angle is estimated through the aligned segments. We evaluate four state-of-the-art landmark localization schemes with the proposed roll-angle estimation initialization in the benchmark dataset. The proposed method boosts the performance of landmark localization in general, especially for cases with large head pose. Moreover, the proposed unsupervised roll-angle estimation method outperforms the standard supervised methods, such as random forest and support vector regression by 41.6% and 47.2%, respectively.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412C (15 March 2019); doi: 10.1117/12.2523111
Show Author Affiliations
Cheng Li, Technische Univ. Eindhoven (Netherlands)
Arash Pourtaherian, Technische Univ. Eindhoven (Netherlands)
W. E. Tjon a Ten, Technische Univ. Eindhoven (Netherlands)
Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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